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ALEKSANDROVA Marharyta

Main Referenced Co-authors
Chertov, Oleg (13)
Boyer, Anne (8)
Brun, Armelle (7)
ENGEL, Thomas  (4)
Kap, Benjamin  (2)
Main Referenced Keywords
academic analytics (1); Additive Noise Models (1); Causal Learning (1); classification (1); Noise Level (1);
Main Referenced Disciplines
Computer science (18)
Engineering, computing & technology: Multidisciplinary, general & others (1)

Publications (total 19)

The most downloaded
280 downloads
Kap, B., Aleksandrova, M., & Engel, T. (2021). Causal Identification with Additive Noise Models: Quantifying the Effect of Noise [Paper presentation]. 10èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes (JFRB-2021). https://hdl.handle.net/10993/49343

The most cited

14 citations (WOS)

Tkachenko, P., Kriukova, G., Aleksandrova, M., Chertov, O., Renard, E., & Pereverzyev, S. V. (2016). Prediction of nocturnal hypoglycemia by an aggregation of previously known prediction approaches: proof of concept for clinical application. Computer Methods & Programs in Biomedicine, 134, 179--186. doi:10.1016/j.cmpb.2016.07.003 https://hdl.handle.net/10993/33400

Kap, B., Aleksandrova, M., & Engel, T. (2022). The Effect of Noise Level on the Accuracy of Causal Discovery Methods with Additive Noise Models. Communications in Computer and Information Science, 1530. doi:10.1007/978-3-030-93842-0_7
Peer reviewed

Aleksandrova, M., & Chertov, O. (11 July 2021). SCR-Apriori for Mining ‘Sets of Contrasting Rules’. Studies in Fuzziness and Soft Computing, 393, 77-89. doi:10.1007/978-3-030-47124-8_7
Peer reviewed

Aleksandrova, M., & Chertov, O. (July 2021). How Nonconformity Functions and Difficulty of Datasets Impact the Efficiency of Conformal Classifiers [Poster presentation]. Workshop on Distribution-Free Uncertainty Quantification at ICML 2021.

Ruppert, J., Aleksandrova, M., & Engel, T. (July 2021). 𝑘-Pareto Optimality for Many-Objective Genetic Optimization [Poster presentation]. GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion. doi:10.1145/3449726.3462732

Aleksandrova, M., & Chertov, O. (2021). Impact of model-agnostic nonconformity functions on efficiency of conformal classifiers: an extensive study. Proceedings of Machine Learning Research, 152.
Peer Reviewed verified by ORBi

Kap, B., Aleksandrova, M., & Engel, T. (2021). Causal Identification with Additive Noise Models: Quantifying the Effect of Noise [Paper presentation]. 10èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes (JFRB-2021).

Mitseva, A., Aleksandrova, M., Engel, T., & Panchenko, A. (2020). Security and Performance Implications of BGP Rerouting-resistant Guard Selection Algorithms for Tor. In Security and Performance Implications of BGP Rerouting-resistant Guard Selection Algorithms for Tor.
Peer reviewed

Roussanaly, A., Aleksandrova, M., & Boyer, A. (2020). BacAnalytics: A Tool to Support Secondary School Examination in France. In 25th International Symposium on Intelligent Systems (ISMIS 2020).
Peer reviewed

Aleksandrova, M., Chertov, O., Brun, A., & Boyer, A. (2017). Contrast classification rules for mining local differences in medical data. In Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2017 9th IEEE International Conference on. doi:10.1109/IDAACS.2017.8095213
Peer reviewed

Tkachenko, P., Kriukova, G., Aleksandrova, M., Chertov, O., Renard, E., & Pereverzyev, S. V. (2016). Prediction of nocturnal hypoglycemia by an aggregation of previously known prediction approaches: proof of concept for clinical application. Computer Methods & Programs in Biomedicine, 134, 179--186. doi:10.1016/j.cmpb.2016.07.003
Peer reviewed

Aleksandrova, M., Brun, A., Chertov, O., & Boyer, A. (2016). Automatic Identification of Trigger Factors: a Possibility for Chance Discovery. In 2nd European Workshop on Chance Discovery and Data Synthesis (EWCDDS16).
Peer reviewed

Aleksandrova, M., Brun, A., Boyer, A., & Chertov, O. (2016). Identifying representative users in matrix factorization-based recommender systems: application to solving the content-less new item cold-start problem. Journal of Intelligent Information Systems. doi:10.1007/s10844-016-0418-3
Peer Reviewed verified by ORBi

Aleksandrova, M., Brun, A., Chertov, O., & Boyer, A. (2016). Sets of Contrasting Rules: A Supervised Descriptive Rule Induction Pattern for Identification of Trigger Factors. In Tools with Artificial Intelligence (ICTAI), 2016 IEEE 28th International Conference on. doi:10.1109/ICTAI.2016.69
Peer reviewed

Aleksandrova, M., Brun, A., Chertov, O., & Boyer, A. (2016). Sets of Contrasting Rules to Identify Trigger Factors. In ECAI. doi:10.3233/978-1-61499-672-9-1728
Peer reviewed

Aleksandrova, M., Brun, A., Boyer, A., & Chertov, O. (2014). Search for user-related features in matrix factorization-based recommender systems. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2014), PhD Session Proceedings.
Peer reviewed

Brun, A., Aleksandrova, M., & Boyer, A. (2014). Can Latent Features Be Interpreted as Users in Matrix Factorization-Based Recommender Systems? In Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)-Volume 02. doi:10.1109/WI-IAT.2014.102
Peer reviewed

Chertov, O., & Aleksandrova, M. (2013). Using association rules for searching levers of influence in census data. Procedia Social and Behavioral Sciences, 73, 475--478. doi:10.1016/j.sbspro.2013.02.079
Peer reviewed

Chertov, O., & Aleksandrova, M. (2013). Fuzzy clustering with prototype extraction for census data analysis. In Soft Computing: State of the Art Theory and Novel Applications (pp. 289--313). Springer.
Peer reviewed

Chertov, O., Tavrov, D., Pavlov, D., Aleksandrova, M., & Volodymyr, M. (2010). Group methods of data processing. Lulu. com.

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